15 Essential Machine Learning Interview Questions (with answers)

Machine learning interview questions are a vital part of the data science interview and the way to becoming a data scientist, machine learning engineer or data engineer. There are some answers to go along with them so you don’t get confused. You’ll be able to do well in any job interview with machine learning interview questions after going through with this piece.

1. What is Machine learning?

Machine learning is a field of computer science that deals with system programming to learn and improve with experience.

For example: Robots are coded so that they can perform the task based on data they collect from sensors. It robotically learns programs from data.

2. Mention the difference between Data Mining and Machine learning?

Data mining: It is defined as the process in which the unstructured data tries to abstract knowledge or unknown interesting patterns.  During this machine process, learning algorithms are used.

Machine learning: It relates with the study, design and development of the algorithms that give processors the ability to learn without being openly programmed.

3. What is ‘Overfitting’ in Machine learning?

In machine learning, when a statistical model defines random error of underlying relationship ‘overfitting’ occurs.  When a model is exceptionally complex, overfitting is generally observed, because of having too many factors with respect to the number of training data types. The model shows poor performance which has been overfit.

4. Why overfitting happens?

The possibility of overfitting happens as the criteria used for training the model is not the same as the criteria used to judge the efficiency of a model.

5. What is inductive machine learning?

The inductive machine learning implicates the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.

6. What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are:

• Supervised Learning
• Semi-supervised Learning
• Unsupervised Learning
• Transduction
• Reinforcement Learning

7. What is the standard approach to supervised learning?

Split the set of example into the training set and the test is the standard approach to supervised learning is.

8. What is not Machine Learning?

• Rule based inference
• Artificial Intelligence

9. In what areas Pattern Recognition is used?

Pattern Recognition can be used in the following areas:

• Computer Vision
• Data Mining
• Speech Recognition
• Informal Retrieval
• Statistics
• Bio-Informatics

10. What is ensemble learning?

To solve a specific computational program, numerous models such as classifiers are strategically made and combined. This process is known as ensemble learning.

11. Which method is frequently used to prevent overfitting?

Isotonic Regression is used to prevent an overfitting problem.

12. What is Model Selection in Machine Learning?

The process of choosing models among diverse mathematical models, which are used to define the same data set is known as Model Selection. It is applied to the fields of statistics, data mining and machine learning.

13. How can you avoid overfitting?

By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. But if you have a small database and you are forced to come with a model based on that. In such situation, you can use a technique known as cross validation. In this method the dataset splits into two section, testing and training datasets, the testing dataset will only test the model while, in training dataset, the data points will come up with the model.

In this technique, a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. The idea of cross validation is to define a dataset to “test” the model in the training phase.

14. What are the five popular algorithms of Machine Learning?

Five popular algorithms are:

• Decision Trees
• Probabilistic networks
• Neural Networks (back propagation)
• Support vector machines
• Nearest Neighbor

15. What are the three stages to build the hypotheses or model in machine learning?

• Model building
• Applying the model
• Model testing. Read More

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About the Author

Shivali Sharma

Shivali is a Senior Content Creator at Multisoft Virtual Academy, where she writes about various technologies, such as ERP, Cyber Security, Splunk, Tensorflow, Selenium, and CEH. With her extensive knowledge and experience in different fields, she is able to provide valuable insights and information to her readers. Shivali is passionate about researching technology and startups, and she is always eager to learn and share her findings with others. You can connect with Shivali through LinkedIn and Twitter to stay updated with her latest articles and to engage in professional discussions.